A specialized research assistant that transforms parsed scientific papers into structured, high-fidelity research cards for deeper academic insight.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install paper-card-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install paper-card-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Paper Card Analyzer?
The Paper Card Analyzer is a sophisticated technical tool designed to work within the ecosystem of Openclaw Skills to bridge the gap between raw document parsing and actionable research synthesis. It specifically targets the output of paper-parse workflows, ingesting markdown content and metadata to generate a comprehensive, ten-section research overview. This skill is engineered for researchers who need to maintain a high-density knowledge base of scientific literature without manual summarization.
By leveraging natural language analysis, this skill ensures that every claim is grounded in the source text, maintaining a strict boundary between author-reported results and independent analyst assessment. It automates the creation of both human-readable Markdown summaries and machine-readable JSON data, making it an essential component for any automated research pipeline utilizing Openclaw Skills.
Paper Card Analyzer Use Cases
- Rapidly summarizing a high volume of scientific papers during literature reviews.
- Generating structured metadata for internal research databases or personal knowledge management systems.
- Performing gap analysis by identifying limitations and threats to validity in recent publications.
- Assessing the reproducibility of academic papers based on reported experimental setups and artifacts.
How Paper Card Analyzer Works
- The skill identifies a pair of target files, specifically the *_content.md and *_parsed.json files generated by a previous parsing step.
- It extracts metadata such as title, figure counts, and publication details from the JSON source.
- The agent performs a deep scan of the markdown content, categorizing evidence into sections like methodology, ablation studies, and results.
- A research-oriented card is drafted, following a strict ten-section hierarchy to ensure consistency across the research library.
- The skill generates and saves paper-card.md, paper-card.json, and a feedback log in the same directory as the source files.
- It enters a human-in-the-loop revision cycle, allowing the user to refine the analysis until the output meets technical requirements.
Paper Card Analyzer Setup
To integrate this capability into your environment, ensure you have the paper-parse output ready. Within your agent interface, you can invoke the logic associated with Openclaw Skills to begin the analysis:
# No external script execution required.
# Simply provide the folder path containing:
# <filename>_content.md
# <filename>_parsed.json
Paper Card Analyzer Data Schema & Taxonomy
The skill organizes its output into a highly structured format to support both reading and programmatic querying.
| File Name | Purpose | Format |
|---|---|---|
paper-card.md |
Human-readable research summary | Markdown |
paper-card.json |
Machine-readable metadata for databases | JSON |
paper-card-feedback.md |
Audit trail of revisions and user edits | Markdown |
JSON Schema Keys:
paper_snapshot: Core bibliographic data.core_contributions: List of specific novelties.reproducibility_notes: Known/unknown variables for independent validation.limitations_and_threats_to_validity: Risk assessment of the findings.
Paper Card Analyzer Advanced Features
- Multi-format output: Synchronized generation of Markdown and JSON files for every revision round.
- Reliability Protocol: Built-in self-check mechanism to flag unsupported factual claims and ensure metric accuracy.
- Versioned Feedback: Maintains a paper-card-feedback.md log to track the evolution of the analysis and user-requested changes.
- Consistency Engine: Enforces a fixed section order across all processed papers, making the results highly compatible with Openclaw Skills data aggregation tools.
SKILL.md
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